Bridging the Gap Between Academia and Industry in Machine Learning Software Defect Prediction: Thirteen Considerations

This experience paper describes thirteen considerations for implementing machine learning software defect prediction (ML SDP) in vivo. Specifically, we provide the following report on the ground of the most important observations and lessons learned gathered during a large-scale research effort and introduction of ML SDP to the system-level testing quality assurance process of one of the leading telecommunication vendors in the world — Nokia. We adhere to a holistic and logical progression based on the principles of the business analysis body of knowledge: from identifying the need and setting requirements, through designing and implementing the solution, to profitability analysis, stakeholder management, and handover. Conversely, for many years, industry adoption has not kept up the pace of academic achievements in the field, despite promising potential to improve quality and decrease the cost of software products for many companies worldwide. Therefore, discussed considerations hopefully help researchers and practitioners bridge the gaps between academia and industry.